1. Field of the Invention
The present invention generally relates to systems and methods for modeling choice scenarios where a decision-maker is capable of choosing one of multiple alternatives. More particularly, the present invention relates to systems and methods which implement discrete choice models that capture proximate covariance property of alternatives to model choice scenarios where alternative competition is a function of alternative proximity along some attribute dimension.
2. Discussion of the Background
The following references (“Reference(s)”), the entire contents of all of which is incorporated herein by references, provide a comprehensive listing of publications facilitating the understanding of, but are not limiting of, certain exemplary embodiments of the present invention:    1. Brown, S. L., and W. S. Watkins. The Demand for Air Travel: A Regression Study of Time-Series and Cross-Sectional Data in the U.S. Domestic Market. Highway Research Record, No. 213, 1968, pp. 21-34.    2. English, J. M., and G. L. Kernan. The Prediction of Air Travel and Aircraft Technology to the Year 2000 Using the Delphi Method. Transportation Research, Vol. 10, No. 1, 1976, pp. 1-8.    3. Transportation Research Circular 348. Aviation Forecasting Methodology: A Special Workshop. Transportation Research Board, National Research Council, Washington, D.C., 1989.    4. Mumayiz, S. A., and R. W. Pulling. Forecasting Air Passenger Demand in Multi-airport Regions. Proceedings of the Transportation Research Forum, TRF, Arlington, Va., USA, 1992.    5. Brown, S. L., and W. S. Watkins. Measuring Elasticities of Air Travel from New Cross-sectional Data. Proceedings, Business and Economic Statistics Section of the American Statistical Association, 1971, pp. 310-315.    6. Verleger, P. K. Jr. Models of the Demand for Air Transportation. Bell Journal of Economics and Management Science, Vol. 3, No. 2, 1972, pp. 437-457.    7. De Vany, A. S., and E. H. Garges. A Forecast of Air Travel and Airport and Airway Use in 1980. Transportation Research, Vol. 6, No. 1, 1972, pp. 1-18.    8. Douglas, G. W., and J. C. Miller III. Economic Regulation of Domestic Air Transport: Theory and Policy. The Brookings Institution, Washington, D.C., 1974.    9. De Vany, A. The Revealed Value of Time in Air Travel. Review of Economics and Statistics, Vol. 56, No. 1, 1974, pp. 77-82.    10. Kanafani, A. K., and S-L Fan. Estimating the Demand for Short-haul Air Transport Systems. In Transportation Research Record. Journal of the Transportation Research Board, No. 526, TRB, National Research Council, Washington D.C., 1974, pp. 1-15.    11. De Vany, A. S. The Effect of Price and Entry Regulation on Airline Output, Capacity and Efficiency. The Bell Journal of Economics, Vol. 6, No. 1, 1975, pp. 327-345.    12. Ippolito, R. A. Estimating Airline Demand with Quality of Service Variables. Journal of Transport Economics and Policy, Vol. 15, No. 1, 1981, pp. 7-15.    13. Anderson, J. E., and M. Kraus. Quality of Service and the Demand for Air Travel. Review of Economics and Statistics, Vol. 63, No. 4, 1981, pp. 533-540.    14. Abrahams, M. A Service Quality Model of Air Travel Demand: An Empirical Study. Transportation Research—Part A, Vol. 17, No. 5, 1983, pp. 385-393.    15. Reiss, P. C., and P. T. Spiller. Competition and Entry in Small Airline Markets. Journal of Law and Economics, Vol. 32, No. 2, 1989, pp. S179-S202.    16. Dresner, M., J-S C. Lin, and R. Windle. The Impact of Low-cost Carriers on Airport and Route Competition. Journal of Transport Economics and Policy, Vol. 30, No. 3, 1996, pp. 309-328.    17. Corsi, T., M. Dresner, and R. Windle. Air Passenger Forecasts: Principles and Practices. Journal of the Transportation Research Forum, Vol. 36, No. 2, 1997, pp. 42-62.    18. Skinner, R. E. Jr. Airport Choice: An Empirical Study. Transportation Engineering Journal, Vol. 102, No. TE4, 1976, pp. 871-882.    19. Augustinus, J. G., and S. A. Demakopoulos. Air Passenger Distribution Model for a Multiterminal Airport System. In Transportation Research Record: Journal of the Transportation Research Board, No. 673, TRB, National Research Council, Washington, D.C., 1978, pp. 176-180.    20. Harvey, G. Airport Choice in a Multiple Airport Region. Transportation Research—Part A, Vol. 21, No. 6, 1987, pp. 439-449.    21. Ashford, N., and M. Benchemam. Passengers' Choice of Airport: An Application of the Multinomial Logit Model. In Transportation Research Record: Journal of the Transportation Research Board, No. 1147, TRB, National Research Council, Washington, D.C., 1987, pp. 1-5.    22. Furuichi, M., and F. S. Koppelman. An Analysis of Air Travelers' Departure Airport and Destination Choice Behavior. Transportation Research—Part A, Vol. 28, No. 3, 1994, pp. 187-195.    23. Windle, R., and M. Dresner. Airport Choice in Multiple-airport Regions. Journal of Transportation Engineering, Vol. 121, No. 4, 1995, pp. 332-337.    24. Suzuki, Y., M. R. Crum, and M. J. Audino. Airport Choice, Leakage, and Experience in Single-airport Regions. Journal of Transportation Engineering, Vol. 129, No. 2, 2003, pp. 212-218.    25. Hess, S., and J. W. Polak. Mixed Logit Modeling of Airport Choice in Multi-airport Regions. Journal of Air Transport Management, Vol. 11, No. 2, 2005, pp. 59-68.    26. Basar, G., and C. Bhat. A Parameterized Consideration Set Model for Airport Choice: An Application to the San Francisco Bay Area. Transportation Research—Part B, Vol. 38, No. 10, 2004, pp. 889-904.    27. Nason, S. D. The Airline Preference Problem: An Application of Disaggregate Logit. Presented at the AGIFORS Symposium, Santa Barbara, Calif., USA, 1981.    28. Morash, E. A., and J. Ozment. The Strategic Use of Transportation Time and Reliability for Competitive Advantage. Transportation Journal, Vol. 36, No. 2, 1996, pp. 35-46.    29. Suzuki, Y., J. Tyworth, and R. Novack. Airline Market Share and Customer Service Quality: A Reference-dependent Model. Transportation Research—Part A, Vol. 35, No. 9, 2001, pp. 773-788.    30. Ghobrial, A., and S. Y. Soliman. An Assessment of Some Factors Influencing the Competitive Strategies of Airlines in Domestic Markets. International Journal of Transport Economics, Vol. 19, No. 3, 1992, pp. 247-258.    31. Nako, S. M. Frequent Flyer Programs and Business Travellers: An Empirical Investigation. The Logistics and Transportation Review, Vol. 28, No. 4, 1992, pp. 395-414.    32. Proussaloglou, K., and F. S. Koppelman. Air Carrier Demand: An Analysis of Market Share Determinants. Transportation, Vol. 22, No. 4, 1995, pp. 371-388.    33. Yoo, K., and N. Ashford. Carrier Choices of Air Passengers in Pacific Rim: Using Comparative Analysis and Complementary Interpretation of Revealed Preference and Stated Preference Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1562, TRB, National Research Council, Washington, D.C., 1996, pp. 1-7.    34. Algers, S., and M. Beser. A Model for Air Passengers Choice of Flight and Booking Class—A Combined Stated Preference and Reveled Preference Approach. Presented at the ATRG Conference, Vancouver, Canada, 1997.    35. Proussaloglou, K., and F. S. Koppelman. The Choice of Air Carrier, Flight, and Fare Class. Journal of Air Transport Management, Vol. 5, No. 4, 1999, pp. 193-201.    36. Bruning, E., and V. Rueda. The Importance of Frequent Flyer Programs as a Barrier to Entry into Air Travel Markets. Journal of Transportation Law, Logistics and Policy, Vol. 67, No. 4, 2000, pp. 367-380.    37. Morrison, M. Aggregation Biases in Stated Preference Studies. Australian Economic Papers, Vol. 39, No. 2, 2000, pp. 215-230.    38. Murphy, J. J., P. G. Allen, T. H. Stevens, and D. Weatherhead. A Meta-Analysis of Hypothetical Bias in Stated Preference Valuation. Environmental and Resource Economics, In Press.    39. OAG Worldwide Limited. Official Airline Guide. Bedfordshire, LU5 4HB, United Kingdom, 2001.    40. Data Base Products, Inc. Superset. Dallas, Tex., USA, 2001.    41. Aptech Systems, Inc. GAUSS. Maple Valley, Wash., USA, 2004.    42. Coldren, G. M., and F. S. Koppelman. Modeling the Competition among Air-travel Itinerary Shares: GEV Model Development. Transportation Research—Part A, Vol. 39, No. 4, 2005, pp. 345-365.    43. Bresnahan, T. F., S. Stem, and M. Trajtenberg. Market Segmentation and the Sources of Rents from Innovation: Personal Computers in the Late 1980s. RAND Journal of Economics, Vol. 28, No. 0 (Special Issue), 1997, pp. S17-S44.    44. McFadden, D. Modeling the Choice of Residential Location. In Transportation Research Record Journal of the Transportation Research Board, No. 673, TRB, National Research Council, Washington, D.C., 1978, pp. 72-77.    45. Small, K. A. A Discrete Choice Model for Ordered Alternatives. Econometrica, Vol. 55, No. 2, 1987, pp. 409-424.    46. Bhat, C. R. Analysis of Travel Mode and Departure Time Choice for Urban Shopping Trips. Transportation Research—Part B, Vol. 32, No. 6, 1998, pp. 361-371.    47. Small, Kenneth A., Approximate Generalized Extreme Value Models of Discrete Choice. Journal of Econometrics 62, pp. 351-382, North-Holland (1994).
At the outset, it is noted that the present invention is applicable to modeling of various choice scenarios, including, but not limited to transportation, consumer products and services, financial products, residential locations, etc. In this following description, certain exemplary aspects of the present invention, as well as the background, are described in the context of air-travel itineraries for the sake of simplicity and clarity of understanding, and by no means as limiting of the scope of the present invention.
Thus, for example, in the context of airline industry, air travelers are presented with a number of choice scenarios in the form of itinerary service characterized by, for example, level-of-service, connection quality, carrier attributes, aircraft (or other carrier) type and departure time. An air travelers is an example of a decision-maker that can choose a single travel itinerary between two destinations (two airports) from among a plurality of different itineraries. The choice of travel itineraries by the air travelers directly impact the number of passengers expected to travel on each itinerary between any airport-pair, and therefore, predicting the choice of itineraries aids carriers in numerous strategic-planning decisions essential for revenue management, schedule efficiency and profitability.
Conventional aviation demand studies have typically either forecasted air-travel demand for a given level of aggregation (for example, system (see References 1, 2, 3), metropolitan region (see Reference 4), city (airport) pair (see References 1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17), airport (see References 18, 19, 20, 21, 22, 23, 24, 25, 26)) or dealt with the allocation of air-travel volumes to air-carriers at a given level of aggregation. Air-carrier allocation studies in the literature typically identify relationships between airline service attributes and the allocation of air-travel volumes. Air-travel demand allocation studies have focused on air-carrier share at the system (see References 27, 28, 29), airport-pair (see References 30, 31, 32) or point-to-point (nonstop) flight share level (see References 33, 34, 35, 36) but not at the itinerary level.
All the above mentioned studies fall into at least one of the following categories: 1) studies based on data with a high level of geographic aggregation, 2) studies employing surveys with a very limited range of airport-pairs or 3) studies based on stated preference data which may be subject to bias (see References 37, 38). Additionally, a major limitation of these studies is their failure to model air-travel demand at the level of individual itineraries, the products that are ultimately purchased by the air travelers.
The above-noted limitations are likewise found in systems and methods of modeling consumer choice behavior in other transportation-related industries, product and services supply industries, financial service supply industries, and other industries where modeling of consumer choice behavior may be used to, for example, facilitate revenue management, scheduling efficiency and profitability.